ENISA’s International Strategy 2026: A Practical Guide to AI Security, Zero‑Trust for Machine Identities, and Global Cyber Defense

If your company adopted AI last year, you didn’t just add a tool—you added a new attack surface with its own identities, plugins, data supply chains, and a mind for automation. Now the EU’s cyber agency has drawn a line in the sand. ENISA’s new International Strategy 2026 puts AI security front and center and asks a hard question: can you see, govern, and defend what your AI is doing in real time?

In this deep dive, we unpack what ENISA is signaling, why agentic AI changes the threat model, and how to align your security program—today—with practical controls for models, data pipelines, plugins, and the non-human identities your AI quietly creates. Expect concrete steps, links to standards you can adopt now, and a 90/180/365-day roadmap to turn this strategy into action.

What ENISA Just Announced—and Why It Matters

ENISA’s International Strategy 2026 outlines how the EU will collaborate globally on emerging threats with AI at the forefront. The strategy emphasizes:

AI’s dual role: a force multiplier for both defense and offense
Secure AI development and governance
Supply chain risk mitigation for models, data, and tooling
International standards for “agentic” systems that can plan and act
Response to a 2025 surge in AI-targeted and AI-enabled incidents
Zero-trust for non-human identities (service accounts, agents, bots)
Visibility into agent behaviors and resilience against permissioned abuse

You can read the strategy summary here: ENISA International Strategy 2026

The theme is unmistakable: AI is now part of core cybersecurity, not an R&D sidebar. ENISA positions itself as a coordinator across borders to align threat intelligence, capacity building, and policy for ethical, secure AI—especially as nation-states weaponize automation, deepfakes, and adversarial machine learning.

Why AI Changes the Threat Model

AI is dual-use by design

Defensive uplift: anomaly detection, incident triage, fraud detection, and faster containment.
Offensive uplift: automated phishing at industrial scale, targeted social engineering with deepfakes, rapid tooling development, and data exfiltration via clever prompt injection and tool abuse.

Agentic systems expand the blast radius

Agentic AI doesn’t just predict—it initiates. With tools and plugins, AI can: – Read or write data in production systems – Move funds, create tickets, send emails, call APIs – Chain actions autonomously to achieve a goal

That autonomy increases risk-by-default. A bad prompt, a poisoned dataset, or a compromised plugin can cause real-world side effects quickly.

Non-human identities are the new perimeter

Bots, service principals, agents, and API keys act as “users.” Without zero-trust controls, these identities become the soft spot adversaries exploit. You need IAM, policy, and observability for machines just as you do for people.

The Strategy’s Pillars: What to Expect from ENISA

1) Secure AI development and lifecycle

Shift-left security in data collection, labeling, training, evaluation, and deployment
Red teaming and safety evaluations for models and agent toolchains
Human-in-the-loop decisioning for high-impact actions

See guidance like NCSC’s Guidelines for Secure AI System Development and NIST AI Risk Management Framework.

2) Supply chain risk and provenance

Vet and monitor third-party models, plugins, datasets, and vector databases
Track model lineage, versions, and dependencies (Model/AI BOMs)
Implement content provenance and auditability across data flows

Explore OWASP Top 10 for LLM Applications and OpenSSF practices for open-source risk management.

3) Standards for agentic systems

Norms for safe tool use and constrained autonomy
Behavioral transparency and logging for agent decisions
Interoperable controls and attestations across borders

Relevant references include ISO/IEC 42001 for AI management systems and MITRE ATLAS adversary tactics for AI.

4) Threat intelligence and capacity building

Cross-border sharing on AI-enabled TTPs (e.g., phishing kits, deepfake ops, model exploitation)
Training for regulators and enterprises on AI governance
Playbooks for incident response with AI in the loop

ENISA’s prior research on AI threats is a helpful primer: ENISA AI Threat Landscape.

5) Policy alignment and ethical deployment

Harmonize with frameworks like the EU AI Act and NIS2
Balance innovation with risk controls, oversight, and accountability
Address nation-state exploitation and hybrid threats

The AI-Enhanced Attacks ENISA Flags

Automated phishing and spear-phishing: Adversaries use AI to generate tailored lures, localize content, and iterate rapidly based on delivery signals.
Deepfake-driven social engineering: Synthetic voice and video for CEO fraud, helpdesk bypass, and KYC evasion.
Adversarial ML:
Prompt injection and tool abuse (influenced inputs cause malicious executions)
Data poisoning and contamination of training or RAG corpora
Evasion attacks against detectors and classifiers

These are not hypotheticals—the uptick in 2025 showed real operational impacts. The key shift: your detection, identity, and response stack must now include AI-aware controls.

What Enterprises Should Do Now: A 12-Point Action Plan

1) Treat AI as part of your security architecture, not a pilot

Add AI assets, agents, and plugins to your CMDB.
Extend your threat model: data sources, prompts, tools, API calls, and outputs.

2) Implement zero-trust for non-human identities (NHIs)

Issue unique, short-lived credentials per agent or workload.
Enforce least privilege, JIT access, and policy-as-code.
Rotate and scope API keys; prefer workload identities over static secrets.

Useful references: SPIFFE/SPIRE for workload identity and Open Policy Agent for policy enforcement.

3) Secure the data pipeline end-to-end

Validate, sanitize, and provenance-tag inputs to RAG and training sets.
Segment data by sensitivity; encrypt everywhere; control PII/PCI/PHI flows.
Monitor for data drift, anomalies, and contamination events.

4) Govern models and agents like software—because they are

Maintain an AI BOM (models, embeddings, tokenizers, datasets, plugins).
Version and attest models; sign artifacts; verify at deploy time.
Keep an approval workflow for tool access and scopes.

5) Put guardrails and constraints around agents

Tool whitelists and per-tool policies (rate, scope, environment).
Require explicit human approval for high-impact actions.
Use input/output filtering to block unsafe prompts and exfil paths.

6) Continuous evaluations and AI red teaming

Test against OWASP LLM risks (prompt injection, data leakage, jailbreaking).
Scenario-based exercises: deepfake vishing, agent tool abuse, invoice fraud.
Track robustness metrics and fix regressions before releases.

See MITRE ATLAS for adversary emulation patterns.

7) Observability for decisions, not just uptime

Capture structured traces: input, tools called, parameters, outputs, policy decisions.
Label content with provenance; store signed, immutable logs.
Feed AI telemetry into SIEM/XDR; alert on abnormal tool use or data access.

8) Human-in-the-loop where it counts

Approval gates for financial transactions, access grants, or customer-impacting comms.
Train operators on AI-specific failure modes and ethical escalation paths.

9) Plugin and third-party risk management

Security review for plugins/tools; verify publisher identity and permissions.
Pen-test and fuzz critical plugins; monitor updates and deprecate promptly.
Treat vector DBs, embedding services, and function routers as Tier-1 assets.

10) Resilience against permissioned abuse

Policy break-glass with post-incident attestations and time-bound tokens.
Transaction limits and anomaly-based holds for sensitive actions.
Containment playbooks to revoke agent credentials instantly.

11) Align with standards and regulation early

Map controls to NIST AI RMF and ISO/IEC 42001.
If in scope, prep for EU AI Act obligations and NIS2 security measures.
Adopt secure-by-design guidance from NCSC and partners.

12) Train and test your people

Anti-deepfake awareness, verification workflows, and callback policies.
Blue team drills for prompt injection, plugin compromise, and model rollback.
Cross-functional governance with legal, risk, and product.

Mapping ENISA’s Strategy to Established Frameworks

Risk management: NIST AI RMF for governance, map to ENISA’s focus on lifecycle and oversight.
Management systems: ISO/IEC 42001 for AI-specific controls and continual improvement.
Application security: OWASP Top 10 for LLM Applications for concrete vuln categories.
Threat intel: MITRE ATLAS to understand offensive AI TTPs and build detections.
EU policy: EU AI Act for risk classes, transparency, and conformity; NIS2 for sectoral resilience.

The win: using these frameworks gives you shared language and evidence when collaborating with regulators, auditors, and international partners.

Building Visibility into Agent Behaviors

To defend agentic AI, you need line-of-sight into what the agent thought, decided, and did.

Decision logs: Record prompts, chain-of-thought summaries (without storing sensitive reasoning verbatim in high-risk contexts), tools called, parameters, and policy outcomes.
Content provenance: Track where data, facts, and attachments originated. Use immutable IDs, hashes, or signatures. Consider C2PA for media authenticity.
Runtime policy checks: Evaluate tool calls against dynamic risk (user, data classification, transaction amount, device, geolocation).
Watermarking and detection: Watermark your generated media where feasible and maintain detectors to spot inbound synthetic content.
Rate and scope limiters: Constrain agent autonomy by bounding actions per session and per context.
Isolation: Run high-risk toolchains in sandboxes; separate write-capable tools from read-only workflows.

Zero-Trust for Non-Human Identities: The New Frontier

Humans aren’t the only users anymore. Your AI stack will spawn service accounts, agents, background jobs, and ephemeral workers. Treat them as first-class citizens in your identity and access program.

Strong identity: Use workload identities (e.g., SPIFFE IDs) instead of long-lived keys; tie identities to attestation of code and environment.
Least privilege: Scope by dataset, tool, and function; deny-by-default; no wildcard permissions.
Short-lived secrets: Rotate often, prefer MTLS or OIDC federation over static keys.
Continuous authorization: Policy-as-code with contextual signals (time, risk score, transaction type).
Segregation of duties: Separate agents that read customer data from those that can take financial actions.
Credential firebreaks: Different trust roots per environment; staged rollouts; kill switches to invalidate agent credentials in seconds.

Resilience Against Permissioned Abuse

Even “approved” actions can be weaponized through influenced inputs. Build resilience assuming a compromised prompt or poisoned data.

Protective guardrails: Business rules that cannot be overridden by prompts (e.g., disallow vendor changes without secondary human approval).
Explainability-on-demand: Provide auditors and responders with sufficient context to reconstruct why the agent acted.
Risk-adaptive friction: Add OTPs, callbacks, or batching for high-risk workflows triggered by agents.
Post-action review: Randomized sampling and QA for sensitive outputs (customer comms, code changes, access grants).
Counterfactual tests: Before executing, simulate the action with redacted data to detect anomalies.

Sector-Specific Notes

Financial services: Dual control for payments; model risk management aligned with SR 11-7 style principles; surveillance for trade advice generation.
Healthcare: PHI segmentation; human review for diagnostic suggestions; provenance and consent tracking for training data.
Public sector/critical infrastructure: Air-gapped or constrained agents; tamper-evident logs; strict supply chain attestations for models and data.

Collaboration Supercharges Defense

ENISA underscores international cooperation to outpace adversaries:

Threat intel: Share AI-specific TTPs via STIX/TAXII; engage ISACs; codify detections for prompt injection and deepfake tradecraft.
Joint exercises: Cross-border tabletop drills for AI incident response.
Harmonized standards: Align procurement requirements to standards like ISO/IEC 42001 and OWASP LLM Top 10 to raise the floor globally.

Useful ENISA resources: ENISA Publications and the new International Strategy 2026.

A 90/180/365-Day Roadmap

Next 90 days
Inventory all AI use cases, agents, plugins, and data sources.
Implement basic NHI controls: unique identities, least privilege, key rotation.
Stand up AI telemetry: tool call logging, prompt/output capture with privacy controls.
Launch anti-deepfake awareness and callback verification for finance/helpdesk.
Next 180 days
Adopt OWASP LLM Top 10 testing and add AI red teaming.
Enforce approval gates for high-impact agent actions.
Build AI BOMs and provenance for RAG/training data; sign model artifacts.
Map controls to NIST AI RMF and begin ISO/IEC 42001 readiness.
Next 365 days
Full zero-trust for NHIs with continuous authorization and attestation.
Sector-aligned resilience: isolation, kill switches, and transaction anomaly holds.
Integrate AI threat intel feeds; participate in industry sharing communities.
Prep for EU AI Act/NIS2 alignment with documented governance and audits.

Common Pitfalls to Avoid

Treating AI as a black box: lack of observability and post-incident reconstructability.
Over-permissioned plugins: broad scopes for convenience that become breach paths.
Ignoring data provenance: contaminated RAG corpora and training sets.
Static secrets: API keys shared across agents without rotation or scoping.
No human gates: allowing agents to execute irreversible actions autonomously.
Security theater: checklists without real-world red teaming or telemetry.

Clear Takeaway

ENISA’s International Strategy 2026 is more than policy—it’s a blueprint to operationalize AI security across borders. The message to enterprises is clear: bring AI under your security program with zero-trust for machine identities, visibility into agent behavior, and resilience against permissioned abuse. Start with inventory and identity, add guardrails and observability, and mature toward standards-aligned governance. The teams that turn these principles into daily practice will not just comply—they’ll outpace adversaries in the age of agentic AI.

FAQs

Q1: What is ENISA’s International Strategy 2026?
A: It’s the European Union Agency for Cybersecurity’s roadmap to coordinate globally on emerging cyber threats, with a major focus on AI. It prioritizes secure AI development, supply chain risk, standards for agentic systems, and cross-border threat intelligence and capacity building. Read it here: ENISA International Strategy 2026.

Q2: Why is AI security different from traditional app security?
A: AI introduces probabilistic behavior, autonomous decision-making via tools/plugins, and new attack surfaces like prompt injection, data poisoning, and model evasion. It also creates non-human identities that need zero-trust controls.

Q3: What does “zero-trust for non-human identities” actually mean?
A: Treat bots, agents, and services like users: unique, short-lived credentials; least privilege; continuous authorization; policy-as-code; and instant revocation. Prefer workload identities and attestation over static API keys.

Q4: How can we defend against deepfake-driven social engineering?
A: Deploy callback verification for sensitive requests, train staff on synthetic media cues, watermark your own generated content where feasible, and use detection tools. Combine user education with process controls (e.g., dual approval).

Q5: What are adversarial inputs and data poisoning?
A: Adversarial inputs craft prompts or data to coerce models into harmful actions (e.g., tool abuse, data leakage). Data poisoning corrupts training or RAG sources to skew outputs. Controls include input sanitization, provenance, evals/red teaming, and constrained tool scopes.

Q6: Which standards should we align with?
A: Start with the NIST AI RMF for risk governance, ISO/IEC 42001 for management systems, OWASP LLM Top 10 for app security, and MITRE ATLAS for attacker tradecraft. Align with the EU AI Act and NIS2 where applicable.

Q7: We’re an SME. What’s the minimal viable AI security stack?
A: Inventory AI use, enforce least privilege for agent identities, rotate keys, approve high-impact actions via human-in-the-loop, log tool calls/prompts/outputs, run OWASP LLM checks, and train staff on deepfakes. Use managed services where possible to reduce overhead.

Q8: How do we measure progress?
A: Track KPIs like percentage of AI use cases inventoried, NHIs with short-lived credentials, coverage of AI evaluations/red teaming, time-to-revoke agent credentials, percentage of plugins with reviewed scopes, and incident MTTR with AI telemetry integrated.

Q9: How does this tie into privacy and ethics?
A: The strategy calls for policy alignment on ethical AI. That translates to purpose limitation, consent and transparency where required, bias testing, human oversight for high-stakes decisions, and documentation for accountability—all alongside security controls.

Q10: Where can I find more guidance from trusted sources?
A: Explore ENISA’s AI resources, NCSC’s secure AI development guidelines, and NIST AI RMF for comprehensive, actionable guidance.

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